Prediction of chemical composition and peroxide value in unground pet foods by near‐infrared spectroscopy

The massive development of the pet food industry in recent years has lead to the formulation of hundreds of canine and feline complete extruded foods with the objective of meeting both the needs of the animals and numerous demands from pet owners. In the meantime, highly variable raw material compositions and the industry's new production techniques oblige manufacturers to monitor all phases of the extrusion process closely in order to ensure the targeted composition and quality of the products. This study aimed at evaluating the potential of infrared technology (visible and near‐infrared spectrophotometer; 570–1842 nm) in predicting the chemical composition and peroxide value (PV) of unground commercial extruded dog foods. Six hundred and forty‐nine commercial extruded dog foods were collected. For each product, an unground aliquot was analysed by infrared instrument while a second aliquot was sent to a laboratory for proximate analysis and PV quantification. The wide range of extruded dog food typologies included in the study was responsible for the wide variability observed within each nutritional trait, especially crude fibre and ash. The mean value of the 208 pet foods sampled for PV quantification was 17.49 mEq O₂/kg fat (min 2.2 and max 94.10 mEq O₂/kg fat). The coefficients of determination in cross‐validation of NIRS prediction models were 0.77, 0.97, 0.83, 0.86, 0.78 and 0.94 for moisture, crude protein, crude fat, crude fibre, ash and nitrogen‐free extract (NFE) respectively. PV prediction was less precise, as demonstrated by the coefficient of determination in cross‐validation (0.66). The results demonstrated the potential of NIRS in predicting chemical composition in unground samples, with lower accuracy for moisture and ash, while PV prediction models suggest use for screening purposes only.     

Feasibility analysis of NIR for detecting sweet corn seeds vigor

This paper explores the feasibility of particle-based detection and grading of seed vigor based on a self-built seed single-granulation device using near infrared spectroscopy (NIRS). Sweet corn with uniform kernel size was used for this study. The seed samples were divided into three types, they were normal seeds, artificially aged seeds and heat-damaged seeds. A 2-part spectral acquisition of each seed were performed, one for the collection of seeds that fall into the detection zone within the separation pipe, another was on the static platform, whose collection was performed on 5 faces of each seed. Partial least squares discriminant analysis (PLS-DA) was used to classify the original data of the seeds. In the 2 parts, the discriminant results of the unprocessed normal seeds and the artificial accelerated aging seeds, the untreated normal seeds and the heat-damaged seeds showed that classification accuracy was higher than 98%. The research indicates that the spectral data of different positions of seeds can reflect their activity information, and it is feasible to detect and classify seeds in real time in the detection area of the separation pipeline.     

花生种子吸胀期间耐低温性及其与品质性状的相关研究

利用55份花生种子进行低温吸胀萌发试验,统计露白率及芽长/种长,筛选耐低温种质,并通过近红外光谱技术测定油酸、亚油酸、棕榈酸、脂肪、蛋白质及蔗糖含量,分析花生种子吸胀期间耐低温性与各项品质性状之间的相关性.结果表明:参试花生种质在2℃96h低温胁迫条件下,露白率≥80%的种质有3份、80%＞露白率≥70%的种质有3份、...     

RAPID DIAGNOSIS OF NUTRIENT ELEMENTS IN FINGERED CITRON LEAF USING NEAR INFRARED REFLECTANCE SPECTROSCOPY

In order to provide references for leaf nutrition diagnosis of fingered citron, the technique of near infrared reflectance spectroscopy (NIRS) was introduced to analyze nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) in the dry-leaf samples of fingered citron. The best calibration model for N was developed with high RSQ_CAL (0.90), SD/SECV (2.73) and low SEC (1.06 mg g^?1), good calibration models were obtained for P, K, Fe and Mn, and no significant correlations were found between the spectra and the individual amounts of Zn and Cu. When tested using a validation set (n = 38), N was well predicted with low values of SEP (1.21 mg g^?1) and high RPD (2.5). The values of SEP and RPD were also acceptable for the external validation of P, Fe and Mn. Near-infrared spectroscopy analysis technique shows potential of diagnosing minerals in fingered citron, particularly for N, P, Fe and Mn.     

应用可见/近红外高光谱成像测定鲑鱼片脂肪含量分布(英文)

脂肪作为一种重要的品质参数,在大西洋鲑鱼片中的分布很不均匀。为寻找一种能替代脂肪化学检测的快速无损的方法,该研究应用可见/近红外高光谱成像测定大西洋鲑鱼片的脂肪含量分布。分别采用可见/短波近红外(400-1100 nm)和近红外(900-1700 nm)系统获取大西洋鲑鱼片样本的高光谱图像。提取样本图像的平均光谱并与其相应的脂肪含量化学值采用偏最小二乘回归(partial least squares regression,PLSR)和最小二乘支持向量机(least-squares support vector machines,LS-SVM)建立相关性模型。为降低高光谱图像的共线性和冗余度,基于竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)分别在可见/短波近红外和近红外光谱区间提取16个(468,479,728,734,785,822,863,890,895,899,920,978,1005,1033,1040,1051 nm)和15个(975,995,1023,1047,1095,1124,1167,1210,1273,1316,1354,1368,1575,1632,1661 nm)特征波长,并分别建立PLSR和LS-SVM模型。特征波长模型的性能优于全波段模型,且近红外区间的特征波长PLSR模型为最优,预测决定系数(R2p)为0.92,预测均方根误差(root mean square error of prediction,RMSEP)为0.92%,剩余预测偏差(residual predictive deviation,RPD)为3.50。最后,将最优模型用于预测高光谱图像上所有像素点的脂肪含量以展示样本上脂肪的分布。此外,还基于该技术对大西洋鲑整鱼片实现了脂肪分布可视化。结果表明高光谱成像技术结合化学计量学方法在大西洋鲑鱼片脂肪的定量和分布可视化上有一定的研究和应用前景。     

基于近红外光谱技术的茶油原产地快速鉴别

为研究茶油原产地溯源问题,维护其市场秩序,促进公平竞争。该文利用近红外光谱技术采集湖南、江西、安徽和浙江4个不同产地茶油的光谱数据,并运用 Savitzky-Golay 平滑（savitzky-golay, SG）、多元散射校正（multiplicative scatter correction, MSC）、一阶导数（first derivation, FD）和矢量归一化（vector normalization, VN）等4种方法对其进行预处理。采用偏最小二乘法（partial least squares, PLS）提取最佳主成分,构建 PLS 回归模型;同时,采用主成分分析（principal component analysis, PCA）和 PLS 算法提取最佳主成分,作为 BP 人工神经网络（BP artificial neural network, BPANN）输入变量,构建 PCA-BPANN 和 PLS-BPANN 模型。以验证集相关系数 RP 和验证集均方根误差 RMSEP 为模型的评价指标,分别优选最佳 PLS 和 BPANN 模型。试验结果表明,SG-PLS-DA 和 SG-PLS-BPANN-DA 模型对未知样本的整体分类准确率均大于90%。其中,SG-PLS-BPANN-DA 的鉴别效果优于前者,其建模集相关系数 RC、均方根误差 RMSEC 分别为0.974、0.170,验证集相关系数 RP、均方根误差 RMSEP 分别为0.972、0.172,对上述两类样本集的总体分类准确率分别为98.15%、95.83%,该模型能较准确鉴别茶油原产地。研究结果可为快速辨别茶油原产地提供参考。     

基于近红外光谱技术的灵芝提取物掺假定量快速无损检测研究

为了探索快速测定灵芝提取物掺假(淀粉)含量的方法,采用近红外光谱扫描掺杂0%、5%、10%、20%、40%、60%、80%淀粉的灵芝提取物,对其光谱进行预处理和波段选择,并结合偏最小二乘法(PLS)建立灵芝提取物掺假定量快速无损检测方法。结果表明,使用多元散射校正(MSC)预处理方法,波数范围8 000~7 500、6 000~5 500和5 000~4 000cm~(-1),主因子数为8时,建立的偏最小二乘法模型的校正决定系数(R_(cal))、校正均方根差(RMSECV)、验证决定系数(R_(val))和验证均方根差(RMSEP)分别为0.9962、0.0249、0.9960和0.0241。运用该模型对验证集样品进行预测并统计分析,可知预测值与实际掺假值之间无显著差异。本研究为灵芝功能食品市场检测提供了方法基础。     

基于固定光栅和CCD技术的近红外光谱分析系统设计

该文设计了一种基于线阵电荷耦合器件和固定光栅的近红外光谱分析系统。主要设计了电荷耦合器件的驱动电路、光谱信号采集电路、固定光栅光学系统、光源控制系统、电荷耦合器件温度控制系统及样品旋转台的控制系统。并利用MPA光谱仪验证了近红外光谱分析方法用于谷物品质检测的可行性,运用多种预处理方法进行了优化,其中采取一阶导数与附加散射校正结合的预处理效果较优。利用CA-06光栅光谱分析仪建立了小麦水分、蛋白的偏最小二乘法模型,验证了仪器性能的稳定性和准确性。     

红茶感官品质及成分近红外光谱快速检测模型建立

以在发酵过程中小叶种工夫红茶为研究对象,分别建立了基于近红外光谱检测技术的感官品质评分和理化品质指标(茶黄素、茶红素、茶褐素、儿茶素和酚氨比)的定量分析模型。在模型建立过程中,探讨了特征变量优选方法对预测模型的影响。首先,对获取的近红外光谱数据进行标准正态变量变换法(standard normal Z transformation,SNV)预处理,进而采用联合区间偏最小二乘回归(synergy interval PLS,Si-PLS)、随机蛙跳算法(shuffled frog leaping algorithm,SFLA)、竞争性自适应权重取样法(competitiveadaptivereweightedsampling,CARS)和连续投影(successive projections algorithm,SPA),筛选出各品质指标的最优特征波长变量;最后基于优选波长分别建立各发酵品质指标的偏最小二乘法(partial least squares regression,PLS)线性预测模型和支持向量机(support vector regression,SVR)非线性预测模型。模型结果比较表明,Si、CARS、SFLA和SPA等变量筛选方法可有效压缩变量,以及进一步提高模型精度。非线性模型的预测均方根误差值(root-mean-square error of prediction,RMSEP)均明显小于PLS模型,相关性系数(correlation coefficient,R)和相对分析误差(relative percent deviation,RPD)均高于PLS模型。对于红茶发酵品质的检测上,非线性模型性能优于线性模型。感官品质、茶褐素和儿茶素的最优变量SVR预测模型的RPD值分别为3.923、3.234和5.462,酚氨比和茶红素模型的RPD值分别为2.815和2.223。除茶黄素的评价模型外(RPD为1.77),基于最优特征波长的各品质指标SVR模型的RPD值均大于2,表明模型具有极好的预测性能。研究结果为实现工夫红茶发酵品质的近红外光谱快速检测的实际应用奠定理论基础。